Surgery – Diagnostic testing – Via monitoring a plurality of physiological data – e.g.,...
Patent
1997-02-28
1998-08-25
Kamm, William E.
Surgery
Diagnostic testing
Via monitoring a plurality of physiological data, e.g.,...
600300, 600483, A61B 500
Patent
active
057978409
DESCRIPTION:
BRIEF SUMMARY
FIELD AND BACKGROUND OF THE INVENTION
The present invention relates to apparatus and method for time dependent power spectrum analysis of physiological signals in general and in particular to time dependent power spectrum analysis of cardio-respiratory physiological signals modulated by the Autonomic Nervous System (ANS).
It is well known that many physiological signals are modulated by the Autonomic Nervous System (ANS). Such physiological signals include cardio-respiratory signals including, respiration, ECG, heart rate (HR), blood pressure (BP), blood flow, vascular resistance, cardiac volume, cardiac cross section, cardiac contractility, peripheral resistance, and the like. Other physiological signals which are not modulated by the ANS include EEG signals, EMG signal, ECoO signals, and the like.
It is also well known that perturbations and/or transient chances which affect the functioning of the ANS affect the physiological signals and vice versa. Common perturbations used in the analysis of autonomic control include changing of posture, tilt, pharmaceutical interventions, deep breaths, vacal maneuvers, hand grip, and others.
Generally speaking, the power spectrum of physiological signals in humans modulated by the ANS can be divided into two frequency ranges: the Low Frequency (LF) range below 0.15 Hz and the High Frequency (HF) range above 0.15 Hz displaying a peak at about 0.2 Hz for adults and a peak at about 0.4 Hz for children. The HF range is mediated by the fast reacting parasympathetic nervous system while the LF range is mediated by both the parasympathetic nervous system and the slower reacting sympathetic nervous system.
Standard spectral analysis by Fourier transform or variations of Auto-Regressive Models have been extensively applied in an attempt to evaluate physiological signals quantitatively under steady state conditions. However, these approaches limit the analysis to rest or restabilization conditions and are not suitable for fast or transient responses which negate the assumption of stationarity.
More recently, approaches have been developed which overcome the stationarity limitations of standard spectral analysis. These approaches can be classified into two main groups: time frequency distributions and time dependent models. Time frequency distributions include the Short Time Fourier Transform (STFT), distributions belonging to the Cohen's class such as the Wigner-Ville Distribution (WVD), Exponential Distribution (ED), and the like. Time dependent models are based on Auto Regressive (AR) or Auto Regressive Moving Average (ARMA) modeling. The disadvantages of the above described approaches include the compromise between frequency resolution and quasi-stationarity for the STFT, the smoothing required by WVD to remove interference terms, the importance of the empirically chosen forgetting factor and the model order of the time dependent AR and ARMA models.
A still more recent advance in the analysis of time dependent signals is described in a paper entitled "The Wavelet Transform, Time Frequency Localization and Signal Analysis" by I. Daubaechies, IEEE Transactions on Information Theory, Vol. 36. No. 5, September 1990 which is incorporated herein be reference as if set forth fully herein. However, up to the present time, this approach has been limited to EEG signals as described in an article entitled "EEG Paroxysmic Activity Detected by Neural Networks after Wavelet Transform Analysis" by Clochon et al. in the European Symposium on Artificial Neural Networks (ESANN) 1993 Proceedings, pg 145-150.
There is therefore a need for apparatus and method for time dependent power spectrum analysis of physiological signals in general and of cardio-respiratory physiological signals modulated by the Autonormic Nervous System (ANS) in particular which overcome the above-mentioned deficiencies.
SUMMARY OF THE INVENTION
The present invention is for an apparatus and method for time dependent power spectrum analysis of physiological signals in general and of cardio-respiratory physiological sig
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Akselrod Solange
Keselbrener Laurence
Friedman Mark M.
Kamm William E.
Layno Carl H.
Ramot University Authority for Applied Research & Industrial Dev
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